Designing models and algorithms for more sophisticated satellite guidance, navigation, and control.
Fuse dynamical systems theory and machine learning to uncover more compact basis functions of complex phenomena.
Enable enhanced spacecraft autonomy by leveraging partially observable Markov decision processes and deep reinforcement learning.
John Martin is an assistant professor of aerospace engineering at the University of Maryland. His research interests include astrodynamics and scientific machine learning. He leads the Machine Learning for Dynamical Systems (MLDS) group, which develops open-source machine learning dynamics models for use in spacecraft guidance, navigation, control, and planning.
PhD Aerospace Engineering, 2023
University of Colorado Boulder
MS in Aerospace Engineering, 2021
University of Colorado Boulder
BSc in Physics and Astronomy, 2018
University of North Carolina at Chapel Hill